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Forecast Model for Box-Office Revenue of Bollywood Feature Films

Prerit Kohli Rajat Taneja Saumya Bansal

Department of Computer Engineering

Netaji Subhas Institute of Technology, University of Delhi

New Delhi, ND 110078, India

Email : p14preritk@iimidr.ac.in; rajattaneja123@gmail.com; saumyabansal03@gmail.com

Abstract:

We consider the technique to forecast the net revenue collections of a feature film.

Previous work on this problem has been addressed majorly to Hollywood films with very

limited work on motion pictures developed by the Hindi Film Industry – Bollywood. In this

piece of work, we use the parameters governing a movie’s revenue and the historical revenue

gross patterns for forecasting. We also show that the model can be used for low budget movies

which are usually left out by technology giants like Google, Twitter etc. due to negligible buzz

for the movie as compared to that for high-budget ones.

Key words: : Forecasting, Machine Learning, Bollywood, Regression model.

Introduction:

Bollywood is the Hindi-language film industry based in Mumbai, India. With 1,000 films

produced annually, it is the world’s largest filmmaking entity. Bollywood gross receipts have

almost tripled since 2004. It generated revenue of around Rs. 15,000 crores in 2011 and this figure

has been growing by 10 percent a year. So, it is of immense importance to study the behavior of

the Indian viewers, the Bollywood industry and the revenue forecast of a particular movie.

In the recent years, forecasting of movie revenues has been linked to the volume of

Google searches, popularity on YouTube, fan-following on Facebook, buzz in the Twitter world,

and so on. But these models may not be very useful in forecasting revenues for novels, music

albums or even low-budget movies that are yet to be released due to lack of buzz associated with

them. Therefore we adopt a model based on the historical sales patterns of similar products, which

can be used efficiently for movies and beyond.

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The study of Bollywood is distinct from the study of other film industries. Apart from the

generic parameters that govern the revenue of a feature film of any film industry, Hindi cinema

has a number of distinct features associated with it which also play a deciding role. The first and

the most important factor is the inclusion of a number of songs within the film, which are released

a few weeks before the movie release date. It is true that these songs top the charts once the movie

hits the theaters but this success pattern is also true the other way around. Some musical movies

like Rockstar [2011] and Aashiqui 2 [2013] have had a major chunk of their revenue collections

associated with the popularity of their music albums among the fans, weeks before the movies

were released. Secondly, it is the “masala” film genre with which many Bollywood films have

been associated. The genre is named after “masala”, the mixture of spices in South Asian cuisine

and depicts the nature of Bollywood mixing genres like action, comedy, drama and romance freely

into one movie. This is done in order to attract audiences with diverse interests.

We, therefore, decided to study all these factors that make Bollywood distinct. Our task is

related to Machine Learning and we have worked upon developing a forecast model with the

primary objective of forecasting the net revenue of Bollywood feature films at the domestic level,

based on early Box Office data. Our intention is to develop a model that is able to assist movie

studios as even a single movie can be the difference between crores of rupees of profit or loss for a

production house in a given year. The model is of intense interest to motion picture exhibitor

chains (retailers) as well, in managing their exhibition capacity with distributors (studios), by

allowing them to project the Box Office potential of the movies they plan to or currently exhibit.

Guidelines:

Gross revenue of a movie mentioned herein refers to the total sales of movie tickets in

India. It does not include auxiliary revenues such as international market revenues, video rentals,

merchandise and soundtrack sales, etc.

The variable of interest is box-office net revenue. Net revenue refers to the actual revenue

a movie makes, after the deduction of taxes. This variable is considered in our study for the

database of previously released films used for forecasting revenue for new feature films.

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Data Set:

The dataset has been extracted from the Internet Movie Database for movie information,

revenue details and cast & crew database from Koimoi.com, and music popularity index from

Top10Bollywood.com. The response a movie receives from film critics have been recorded via

CNN IBN, Hindustan Times, Dainik Bhaskar, Zoom and Bollywood Hungama.

The Machine Learning database covers 100 films released in India in the years 2010-

2014. For power lists on popular and trending members of the film fraternity, movie performances

during the decade 2004-2014 have been considered.

Forecasting Task:

We explore the use of Machine Learning in forecasting the commercial success of a

movie at the Box Office. Machine Learning is a branch of Artificial Intelligence that involves the

study and construction of systems that can learn from data. The Machine learning technique used

is Regression analysis.

The process of regression is used to take into account all the revenue-determining factors

of movies which have already been released. This is done so as to determine the influence of each

parameter that contributes to a film’s Box office performance. The parameters range from general

factors like cast & crew and genre, to specific ones like music-album popularity, competition for

same release date, etc.

General Parameters:

Table 1 enlists the general factors used for forecast analysis, whose default values are constant for

each film considered. Some factors such as Star Power, success of movie franchisee in the past,

type of release (festive season) etc., play a major role in the commercial success of a feature film.

Best Actor (2004-2014)

Trending Actor

Best Actress (2004-2014)

Trending Actress

Best Director (2004-2014)

Promising Director

Production House

Sequel

Trilogy Finale/Extended Trilogy

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Successful Pair (actor-actor)

Successful Pair (actor-director)

Festive Season Release: Diwali/Christmas

Genre

CBFC Rating: U/UA/A

Table 1: List of General Parameters

Movie Specific Parameters:

Table 2 enlists the factors whose values depend on the movie or its release date. Some factors such

as Production and Marketing budget, Music album popularity, Level of competition (movie

releasing on same day/blockbuster next week), Number of screens booked nationwide etc., play a

major role in improving the efficiency of forecast results for a film.

Production and Marketing Budget

Fan-following/Adaptation/Remake

Music Album Popularity

Number of Screens

Publicity/Reviews on Paid-Previews

Level of Competition

Critics’ review

Audience response

Post-release Promotion

Word of Mouth

Entertainment Tax promotion

Table 2: List of Movie-specific Parameters

Implementation:

The model comprises of three methods for revenue forecasting, each applied at three

different stages of a movie’s lifecycle.

The first stage is when the film is completed and sent to the studio. This forecast data is used

by movie studios for purposes such as deciding the prerelease marketing budget, the number of

cinema screens to book, etc.

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Figure 1: Average-Error Graph for implementation of Method 1

The second stage is when the movie prints are sent to the theaters a few days before the

release. This forecast data is used by movie exhibitors for finalizing the number of screens to be

devoted to that movie being released in the next Box Office week.

Figure 2: Average-Error Graph for implementation of Method 2

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The third is at the end of the first weekend of the release date. Usually, more than 25% of

the total revenue is grossed in the first weekend, but the rate at which it decays depends on a

number of factors such as Critics’ reviews, Audience response, magnitude of word of mouth, etc.

Figure 3: Average-Error Graph for implementation of Method 3

Results and Discussion:

We use linear regression to forecast the net earnings of a film, denoted R, based on

parameters P which govern the revenue of a movie. The formula is as follows:

Ri = β1(P1)i + β2(P2)i +... + βn(Pn)i

where Ri, (Pn)i and βn indicate the forecasted revenue of the film i, the value of nth

parameter for film i, the corresponding coefficient of the nth parameter, respectively.

Studios make fewer films that are expected to get a CBFC rating A since they are skewed to

a narrower market, while films with CBFC rating U and U/A have a much larger potential

audience. The U/A rating is quite desirable, as it can pull in both adults and children, and excludes

virtually no one.

Similar to the parameter devoted to Music-album popularity, a number of parameters have

been analyzed which do not come in the list of conventional factors like Best Actor, Trending

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Actress, Production House, etc. Some of them are Number of Screens booked, Competition with

movies sharing the same Release-date and the viral Word of Mouth.

The number of prints/screens booked for a potential blockbuster film has doubled over the

year the past 5 years. Dabangg [2010] booked 1800 screens nationwide while Jai Ho [2014]

booked 3900 screens worldwide, including single screens and multiplexes. But Dabangg

collecting more revenue than Jai Ho is a different issue, pertaining to the “masala” element in a

film.

Next factor considered is the Competition faced by movies sharing the same Release-date. In

India, the Diwali season of 2012 saw the clash of two potential blockbusters Jab Tak Hain Jaan

[2012] and Son Of Sardaar [2012], releasing on the same date. Though both the movies collected

above 100 crores (net) at the Box Office, it did not do justice to the amount of prerelease

marketing done by both of them.

The third factor is the one pertaining to the Word of Mouth publicity surrounding a movie,

with the most prominent example being that of Queen [2014]. Its first weekend net gross was Rs.

10 Crores. Being a low-budget film with a debutant director and a decent star cast, the film would

not have made more than Rs. 20-25 Crores, but it collected a total of Rs. 61 Crores at the Box

Office, which is not possible to forecast without the use of Word of Mouth parameter.

Net revenues of movies displayed on a logarithmic scale to account for the disparity in the

earnings of all movies.

Forecasting Accuracy:

The forecast accuracy for box office grosses increases as we go from Stage 1 (the film is

completed and sent to the studio) to Stage 2 (the movie prints are sent to the theaters a few days

before the release) and finally, Stage 3 (at the end of the first weekend of the release date).

The average errors for Stage 1, 2 and 3 were 36.05%, 19.52% and 11.74% respectively. This

depicts the correlation between the numbers of factors incorporated in the forecast dataset for a

given movie.

References:

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